Measure

precision

Precision is defined as the number of true positive (TP) predictions, divided by the sum of the number of true positives and false positives (TP+FP): $$\text{Precision}=\frac{tp}{tp+fp} \,$$ It is also referred to as the Positive predictive value (PPV). See: http://en.wikipedia.org/wiki/Precision_and_recall Precision is defined only for a specific class value, and should thus be labeled with the class value for which is was computed. Use the mean_weighted_precision for the weighted average over all class values.

Source Code:
WEKA's Evaluation.precision(int classIndex)

/**
* Calculate the precision with respect to a particular class.
* This is defined as
*    * correctly classified positives
* ------------------------------
*  total predicted as positive
*
*
* @param classIndex the index of the class to consider as "positive"
* @return the precision
*/
public double precision(int classIndex) {

double correct = 0, total = 0;
for (int i = 0; i < m_NumClasses; i++) {
if (i == classIndex) {
correct += m_ConfusionMatrix[i][classIndex];
}
total += m_ConfusionMatrix[i][classIndex];
}
if (total == 0) {
return 0;
}
return correct / total;
}

Properties

 Minimum value 0 Maximum value 0 Unit Optimization Higher is better